Triple
T13772168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dentdale |
E330906
|
entity |
| Predicate | mainSettlement |
P13187
|
FINISHED |
| Object |
Dent
Dent is a small historic village in Cumbria, England, known for its cobbled streets, traditional stone buildings, and scenic setting in the Yorkshire Dales National Park.
|
E1059716
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Dent | Statement: [Dentdale, mainSettlement, Dent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dent Context triple: [Dentdale, mainSettlement, Dent]
-
A.
Dent
Dent is a surname most prominently associated with Richard Dent, a Hall of Fame former NFL defensive end for the Chicago Bears.
-
B.
Denti
Denti is an Italian film featuring actor and director Sergio Rubini, known for its darkly comic exploration of relationships and personal neuroses.
-
C.
Zahn
Zahn is a surname most prominently associated with American actor and comedian Steve Zahn, known for his roles in films like "That Thing You Do!" and "Saving Silverman."
-
D.
Muldental
Muldental is the valley region surrounding the Mulde River in Germany, known for its scenic landscapes and small towns.
-
E.
Zahniser
Zahniser is a surname most notably associated with Howard Zahniser, the American environmentalist and principal author of the U.S. Wilderness Act.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Dent Triple: [Dentdale, mainSettlement, Dent]
Generated description
Dent is a small historic village in Cumbria, England, known for its cobbled streets, traditional stone buildings, and scenic setting in the Yorkshire Dales National Park.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dent Target entity description: Dent is a small historic village in Cumbria, England, known for its cobbled streets, traditional stone buildings, and scenic setting in the Yorkshire Dales National Park.
-
A.
Dent
Dent is a surname most prominently associated with Richard Dent, a Hall of Fame former NFL defensive end for the Chicago Bears.
-
B.
Denti
Denti is an Italian film featuring actor and director Sergio Rubini, known for its darkly comic exploration of relationships and personal neuroses.
-
C.
Zahn
Zahn is a surname most prominently associated with American actor and comedian Steve Zahn, known for his roles in films like "That Thing You Do!" and "Saving Silverman."
-
D.
Muldental
Muldental is the valley region surrounding the Mulde River in Germany, known for its scenic landscapes and small towns.
-
E.
Zahniser
Zahniser is a surname most notably associated with Howard Zahniser, the American environmentalist and principal author of the U.S. Wilderness Act.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0235aea881909ab4c721db081b00 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a86907cc8190a6a6b475d08f0dc7 |
completed | May 3, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69f7a968c3508190b1a86accb71b34cf |
completed | May 3, 2026, 8 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7aa2f696081908f48d44bf7271abc |
completed | May 3, 2026, 8:03 p.m. |
Created at: April 9, 2026, 10:10 p.m.